Lipidomics is a new frontier of omics research and offers much promise for new-generation biomarkers for common complex phenotypes such as hyperlipidemia (HL) and cardiovascular diseases (CVDs). HL is a disorder characterized by increased levels of blood lipids and is a well-established risk factor for CVD. Traditional clinical markers for prognosis of hyperlipidemic individuals are inadequate to forecast or diagnose cardiac events. In this expert review, lipidomic profiles from recent HL and CVD studies were compared with the normolipidemic profile prepared from the Standard Reference Material. Our analysis showed that palmitoyl-lysophosphatidylcholine [LPC(16:0)], the most abundant LPC species in normolipidemic plasma, decreases in HL causative conditions such as high-fat diet, obesity, and diabetes. This is accompanied by increase in free fatty acids (FFAs) and ceramides (Cers). HL was also found to be characterized by increase in small-chain, saturated fatty acid content of diacylglycerols, triacylglycerols, and phosphatidylcholines (PCs). These factors were also associated with increased CVD risk. The decrease in LPC(16:0) in HL and CVD is consistent with its role in regulation of peroxisome proliferator-activated receptor alpha, an approved HL drug target that impacts the uptake and oxidation of fatty acids. FFAs are involved in endothelial-dependent nitric oxide production and activation of nuclear factor κB signaling. Cers control uptake and anabolic catabolism of nutrients in tissues. However, additional studies are required to establish the range of normal and disease levels of the identified lipids in different populations and conditions. In all, these observations underscore that lipidomics deserves greater research attention from the biomarker and precision medicine research communities.
The prevalence of acquired hyperlipidemia has increased due to sedentary life style and lipid-rich diet. In this work, a lipid-protein-protein interaction network (LPPIN) for acquired hyperlipidemia was prepared by incorporating differentially expressed genes in obese fatty liver as seed nodes, protein interactions from PathwayLinker, and lipid interactions from STITCH4.0. Cholesterol, diacylglycreol, phosphatidylinositol-bis-phosphate, and inositol triphosphate were identified as core lipids that influence the signaling pathways in the LPPIN. RACα serine/threonine-protein kinase (AKT1) was a highly essential central protein. The gastrin-CREB pathway was greatly enriched; all enriched pathways in the LPPIN showed crosstalk with the phosphatidylinositol-3-kinase-Akt pathway, correlating with the central role of AKT1 in the network. The disease clusters identified in the LPPIN were cardiovascular disease, cancer, Alzheimer's disease, and Type II diabetes. In this context, we note that the commercially approved drug targets for hyperlipidemia in each disease cluster may potentially be repurposed for treatment of the specific disease. We report here top 10 potential drug targets that may mediate progression from hyperlipidemia to the respective disease state. ToppGene Suite was employed to identify candidates followed by a) discarding high closeness centrality nodes, and b) selecting nodes with high bridging centrality. Three potential targets could be mapped to specific disease clusters in the LPPIN. Lipids associated with acquired hyperlipidemia and each disease cluster identified may be useful as prognostic fingerprints. These findings provide an integrative view of lipid-protein interactions leading to acquired hyperlipidemia and the associated diseases, and might prove useful in future translational pharmaceutical research.
The conventional way of characterizing a disease consists of correlating clinical symptoms with pathological findings. Although this approach for many years has assisted clinicians in establishing syndromic patterns for pathophenotypes, it has major limitations as it does not consider preclinical disease states and is unable to individualize medicine. Moreover, the complexity of disease biology is the major challenge in the development of effective and safe medicines. Therefore, the process of drug development must consider biological responses in both pathological and physiological conditions. Consequently, a quantitative and holistic systems biology approach could aid in understanding complex biological systems by providing an exceptional platform to integrate diverse data types with molecular as well as pathway information, leading to development of predictive models for complex diseases. Furthermore, an increase in knowledgebase of proteins, genes, metabolites from high-throughput experimental data accelerates hypothesis generation and testing in disease models. The systems biology approach also assists in predicting drug effects, repurposing of existing drugs, identifying new targets, facilitating development of personalized medicine and improving decision making and success rate of new drugs in clinical trials.
Inflammatory is cascade process triggered by pro-inflammatory cytokines and anti-inflammatory. In the present study anti-inflammatory activity of Stachydrine and Sakuranetin were studied against the inflammatory target proteins IL-6 and TNF-α by using molecular docking analysis. Both compounds showed the good binding with selected target proteins. Compared to Sakuranetin, the Stachydrine have lowest binding energy and good hydrogen bond interactions. Hence results of study indicated that Stachydrine possessed high and specific inhibitory activity on tumor necrosis factor-α and interleukin-6.
A Drug designing is a process in which new leads (potential drugs) are discovered which have therapeutic benefits in diseased condition. With development of various computational tools and availability of databases (having information about 3D structure of various molecules) discovery of drugs became comparatively, a faster process. The two major drug development methods are structure based drug designing and ligand based drug designing. Structure based methods try to make predictions based on three dimensional structure of the target molecules. The major approach of structure based drug designing is Molecular docking, a method based on several sampling algorithms and scoring functions. Docking can be performed in several ways depending upon whether ligand and receptors are rigid or flexible. Hotspot grafting, is another method of drug designing. It is preferred when the structure of a native binding protein and target protein complex is available and the hotspots on the interface are known. In absence of information of three Dimensional structure of target molecule, Ligand based methods are used. Two common methods used in ligand based drug designing are Pharmacophore modelling and QSAR. Pharmacophore modelling explains only essential features of an active ligand whereas QSAR model determines effect of certain property on activity of ligand. Fragment based drug designing is a de novo approach of building new lead compounds using fragments within the active site of the protein. All the candidate leads obtained by various drug designing method need to satisfy ADMET properties for its development as a drug. Insilico ADMET prediction tools have made ADMET profiling an easier and faster process. In this review, various softwares available for drug designing and ADMET property predictions have also been listed.
Background: Even after decades of research, cancer, by and large, remains a challenge and is one of the major causes of death worldwide. For a very long time, it was believed that cancer is simply an outcome of changes at the genetic level but today, it has become a well-established fact that both genetics and epigenetics work together resulting in the transformation of normal cells to cancerous cells. Objective: In the present scenario, researchers are focusing on targeting epigenetic machinery. The main advantage of targeting epigenetic mechanisms is their reversibility. Thus, cells can be reprogrammed to their normal state. Graph theory is a powerful gift of mathematics which allows us to understand complex networks. Methodology: In this study, graph theory was utilized for quantitative analysis of the epigenetic network of hepato-cellular carcinoma (HCC) and subsequently finding out the important vertices in the network thus obtained. Secondly, this network was utilized to locate novel targets for hepato-cellular carcinoma epigenetic therapy. Results: The vertices represent the genes involved in the epigenetic mechanism of HCC. Topological parameters like clustering coefficient, eccentricity, degree, etc. have been evaluated for the assessment of the essentiality of the node in the epigenetic network. Conclusion: The top ten novel epigenetic target genes involved in HCC reported in this study are cdk6, cdk4, cdkn2a, smad7, smad3, ccnd1, e2f1, sf3b1, ctnnb1, and tgfb1.
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